Amphiregulin as a central dynamic network biomarker in inhaled polyhexamethylene guanidine triggered pulmonary fibrosis.

State-level brain dynamics reveal neural correlates of negative-mode rigidity in non-suicidal self-injury.

Functional cortical network alterations in Parkinson's disease with wearing-off revealed by resting-state fNIRS and graph theory.

Understanding how regulatory complexity and constraint shape organismal development remains a central challenge in biology. The developmental hourglass framework posits that mid-embryogenesis-the phylotypic stage-is a period of heightened conservation and coordinated regulatory organization. We test this hypothesis using Zebraformer, a transformer-based language model trained on single-cell transcriptomic data from zebrafish embryos. Zebraformer learns context-sensitive representations that capture temporal progression, anatomical identity, and regulatory relationships, yielding gene and cell embeddings that recapitulate the developmental axis and increasing transcriptional divergence over time. In contrast, attention-derived gene networks reveal a transient reorganization of regulatory architecture during the phylotypic stage, marked by tightly coordinated gene modules, reduced cross-module connectivity, and diminished local redundancy. Sensitivity to perturbation emerges specifically when regulatory interaction structure is taken into account, rather than from perturbation magnitude alone, highlighting that constraint during this stage is embedded in network topology rather than representational fragility. These findings are supported by graph-theoretic metrics and gene ontology enrichment analyses. Together, our results refine the hourglass framework by localizing developmental constraint to the architecture of gene regulatory networks and demonstrate that language models can extract interpretable biological structure from high-dimensional single-cell data.

Homogeneous coupled cell systems with high-dimensional internal dynamics

Asthma's impact on brain function: an investigation of changes in functional connectivity and network topology.

• Novel fusion method combines voltage harmonics using cluster quality weighting. • Perfect topology identification achieved with only 4–6 h of measurement data. • Method remains accurate with 10% measurement error and 60-min time resolution. • High-order harmonics outperform traditional RMS voltage for network identification. • Calinski-Harabasz index weights measurements by their clustering performance. Accurate identification of low-voltage (LV) network topology is becoming increasingly important, as reliable and detailed topological information is vital for effective network operation and precise modelling. Topology identification approaches based on smart-meter data typically rely on RMS voltage, current, and power measurements, which are limited in accuracy due to factors such as time resolution, measurement intervals, and instruments errors. This work introduces a novel methodology for distribution network topology identification through a multi-parametric analysis of smart-meter measurements. The core innovation lies in utilising the Calinski-Harabasz index (CH) as a weighting factor for multi-measurement distance matrices. The proposed framework integrates three distinct classes of measurements: V rms , harmonic components ( V 2 – V 20 ), and THD. The methodology addresses critical challenges in measurement-based topology identification approaches, including high measurement errors, short data collection time intervals, and large time resolution. The resilience of the methodology stems from a hierarchical approach that combines correlation analysis, cluster validation, and graph-theoretic network reconstruction. The results demonstrate significant improvement in the accuracy and robustness of network topology identification, compared to approaches based on single-measurement types.

Fruits and vegetables are key components of the human diet, valued for their unique textures and flavors. In recent years, numerous studies have demonstrated that individual transcription factors (TFs) can simultaneously regulate two biological processes; these TFs are defined as bifunctional TFs. However, systematic reviews on these bifunctional TFs in fruits and vegetables remain limited. This review systematically summarizes current knowledge on bifunctional TFs in fruits and vegetables, focusing on three themes: (i) molecular mechanisms (cis-element diversity, partner switching, posttranslational); (ii) network topology (hubs vs bottlenecks); and (iii) agronomic trade-offs. Meanwhile, the functional conservation and divergence of homologous TFs in different fruits and vegetables have also been investigated. In addition, we elaborate how key TF families, including MYB, bHLH, WRKY, ERF, and NAC, regulate diverse physiological processes in fruits and vegetables via dual mechanisms. We also identify several limitations in the existing literature, such as insufficient understanding of bifunctional regulatory mechanisms, incomplete identification of target genes, and inadequate exploration of crop applications.

Dynamic analysis and immune control strategy of a rumor propagation model considering network topology

FiTNet: Frequency information guided topological relationship enhancement network for X-ray vascular segmentation

An adaptive regularized topological segmentation network integrating inter-class relations and occlusion information for vehicle component recognition

The neurostructural underpinnings of premenstrual dysphoric disorder (PMDD), particularly integrated white matter and network alteration, remain unclear. To identify a core structural network in PMDD by integrating multiple diffusion tensor imaging (DTI)-derived metrics and to develop a predictive model. Prospective case-control study. Forty-two PMDD patients (age: 23.86 ± 1.32 years), diagnosed according to the American Psychiatric Association DSM-5, and 42 healthy controls (age: 23.79 ± 1.72 years). 3.0 T, T1-weighted three-dimensional gradient-echo and echo planar imaging DTI sequences. Microstructural and connectivity features were extracted from DTI using tract-based spatial statistics (TBSS), network-based statistics (NBS), and graph theory analyses. A combined predictive model was constructed by integrating the most stable features from the three single-modality models via least absolute shrinkage and selection operator (LASSO) regression. Group comparisons were performed using two-sample t-tests or Mann-Whitney U tests, with false discovery rate correction. Features were selected using LASSO and integrated to construct a combined model. Model performance was evaluated by the area under the receiver operating characteristic curve (AUC) using leave-one-out cross-validation. p < 0.05 was considered significant. PMDD patients exhibited widespread microstructural and connectivity alterations, including elevated axial diffusivity in the right posterior limb of the internal capsule, enhanced edge connectivity, and altered network topology. The combined model achieved significantly superior predictive performance (AUC = 0.855) compared with the TBSS-based model (AUC = 0.699) and the network-based model (AUC = 0.727), and a higher AUC than the graph-based model (AUC = 0.790). Key predictive features included two enhanced edges originating from the left inferior frontal gyrus and reduced degree centrality of the left inferior occipital gyrus and sulcus. Our DTI-based predictive model showed alterations in brain connections and network properties in the left inferior frontal and inferior occipital regions of PMDD patients. Stage 2.

Complex systems involving multiple oscillatory components are known to exhibit emergent collective spatiotemporal patterns. The turbulent annular combustor is a prime example where the interaction between the acoustic field and the heat release rate fluctuations from multiple flames gives rise to rich spatiotemporal collective behavior. The collective dynamical behavior leads to high-amplitude, self-sustained oscillations in the acoustic field, which are detrimental to the system. In this study, we investigate the transition among various collective dynamical states, including the splay state, the two-cluster state, the in-phase state, the amplitude-modulated two-cluster state, and the quasiperiodic weak chimera, in the acoustic field of a turbulent annular combustor as the control parameter is varied. We also observe the signatures of the impending dynamical state before the transition. We introduce a network-based characterization method to characterize these dynamical states. We construct a functional network among the acoustic pressure fluctuations recorded around the annulus of the annular combustor. We show that each dynamical state has a unique network topology. Further, we demonstrate that the network-based measure can detect the impending transition between states in the annular combustor. This can potentially enable the timely implementation of control actions to prevent such transitions to the collective states.

Adolescence is a critical period for the onset of major depressive disorder (MDD), during which emotional symptoms frequently co-occur with environmental adversity and maladaptive behavioral coping. In China, these processes may be further shaped by left-behind experience (LBE) - a prevalent structural vulnerability in which children grow up separated from migrating parents. To clarify these relationships, we applied network analysis in a clinical sample of 2341 adolescents (Mage = 14.99 years,77.92% female) with MDD. Participants were recruited from 14 psychiatric outpatient clinics across China. The networks examined associations among mental health symptoms (depression, anxiety, stress, sleep problems, self-esteem, loneliness), ecological factors (childhood trauma, peer victimization, social support), and problematic smartphone use (PSU). We further examined whether network topology differed by LBE (19.99% of the sample). In the full sample network, depression and loneliness demonstrated the highest strength centrality, while sleep exhibited the lowest. Loneliness and PSU acted as bridges between mental health symptoms and ecological factors. Network comparison tests revealed no significant between-group global structural differences. Although childhood trauma and PSU appeared more centrally positioned in the left-behind group and social support showed slightly higher centrality in the non-left-behind network, these subtle trends need further confirmation. These findings underscore critical psychological-ecological-behavioral interaction pathways in adolescents with MDD, and suggest loneliness and PSU as promising targets for bridge-informed clinical intervention.

The need for reliable internet connectivity in educational environments is crucial, but is often hampered by inefficient network infrastructure. This study aims to design an optimal Fiber To The Room (FTTR) network topology design based on Gigabit Passive Optical Network (GPON) at SMK NU Ma'arif Kudus, focusing on the efficiency of fiber optic cable installation routes. The research method used is engineering design with a quantitative approach, where the school architectural plan is modeled into a weighted graph. Route optimization is carried out by implementing the Dijkstra Algorithm to find the shortest path from the center node (ODC) to all node termination points (ODP). While node I (ODC) is designated as the starting node because it functions as the network distribution center. The calculation process is carried out by determining the minimum distance from the starting node to all destination nodes (ODP). The calculation results show that the shortest path is divided into two main routes, namely I to C to B to A to D to E and I to F to G to H. The selection of this node is proven to be able to produce a more efficient total distance compared to direct paths in several network segments. Based on these results, it can be concluded that Dijkstra's algorithm is effective in fiber optic network planning because it can optimally determine the path with the minimum distance. The application of this method is expected to assist in decision-making regarding fiber optic network infrastructure planning, making it more efficient and applicable for implementation in school environments.

Beyond coordination and covalent bonds: Stabilization strategies and emerging applications of metal hydrogen-bonded organic frameworks

Altered brain network topology in adolescents with major depressive disorder and bipolar disorder: A resting-state fMRI graph-theoretical and machine learning study.

Introduction Public search behavior provides a high-frequency external attention signal for understanding changes in market expectations in the digital economy. During periods of macroeconomic adjustment and investment uncertainty, search attention may capture shifts in public concern, information demand, and expectation formation. Methods Using Douyin search data on the theme of “investment” in Shandong Province from 4 June 2022 to 1 August 2025, this study constructs a Public Investment Search Network based on the Visibility Graph algorithm. The analysis examines temporal fluctuation, phase-based evolution, network topology, community differentiation, topological indicators, degree distribution, and robustness under alternative network constructions. Results The results show clear phase-based aggregation and divergence in public investment attention. The network exhibits a heavy-tailed degree distribution and small-world-like characteristics. Attention evolves through a cyclical process of concentration, dispersion, and rebalancing under the combined influence of policy stimuli, market fluctuations, and information diffusion. Changes in clustering coefficient, modularity, volatility, and Shannon entropy further reveal the self-organizing features of public investment search behavior. Discussion The findings suggest that the public investment search network provides a structural representation of collective attention and offers supplementary information for monitoring market signals and changes in public expectations. The study describes the structure of public investment-related attention rather than directly testing firm-level investment responses. Future research may combine search-network indicators with firm-level investment, innovation, or financial data to further examine how external attention signals are incorporated into corporate decisions.

Exploring the mechanism of acupuncture for Chronic Atrophic Gastritis based on bioinformatics and network topology strategies.

Porous silica nanostructures were synthesized using various polysaccharides including Tragacanth gum, Guar gum, Arabic gum, Xanthan gum, and Chitosan as environmentally friendly soft templates. The role of biopolymer chemical structure and topology of supramolecular network on the formation of porous silica structures and its effect on the heavy metal adsorption was investigated. The adsorption capacity of the synthesized materials was evaluated for the removal of Cu²⁺, Pb²⁺, Cd²⁺, and Ni²⁺ ions. Among the ions investigated, Cu²⁺ showed the highest adsorption capacity (60.55 mg g-1). The adsorption potential is strongly influenced by adsorbent structure and metal behavior. Guar Gum showed the enhanced adsorption capacity due to the highest specific surface area and the largest total pore volume among all synthesized materials. Adsorption equilibrium were analyzed using nonlinear Langmuir, Freundlich, Temkin, and Sips isotherm models. The Freundlich and Sips models showed the best fitting performance, suggesting heterogeneous adsorption behavior associated with structurally diverse mesoporous silica surfaces. Thermodynamic analysis shows the spontaneous endothermic adsorption process which is predominantly governed by chemisorption in Guar gum and physisorption interactions in other absorbances. Two-Way Analysis of Variance shows the selective affinities of specific adsorbents toward particular metal ions. The synthesized adsorbents also exhibited good regeneration capability, maintaining stable adsorption performance during multiple adsorption-desorption cycles. The obtained results indicate that the supramolecular organization and network topology of natural biopolymer templates play a decisive role in directing silica condensation and controlling the final porous architecture. The environmentally friendly synthesis strategy developed in this work provides an effective route for preparing high-performance mesoporous silica adsorbents for heavy metal removal from wastewater.